The monitoring of multiple channels of real-time data plays a key role in business processes in various domains. For example, in the production context (say, in the Oil and Gas Industry) sensors monitor various parameters and produce information at various temporal granularities. An important reason for this monitoring is to detect abnormal situations in a timely fashion to take corrective action. This monitoring can be done by domain experts but that can be an expensive and inconvenient process especially when this has to be done round-the-clock. For each situation, one can envision building a new system from scratch that mimics the monitoring done by the human expert as a possible solution to this problem. This can be an expensive proposition if there are multiple situations to be considered in each domain. Also, one has to find a way to incorporate domain knowledge related to the channels being monitored and the notion of abnormality in the detection process.
In U.S. Pat. No 6,131,076 a method and system is disclosed for automatically establishing operational parameters of a statistical surveillance system. This is done using transformations of the time dependent data into the frequency domain and using sequential probability ratio test (SPRT).
In U.S. Pat. No. 6,859,739 a model-based surveillance system is disclosed for monitoring or controlling a process or machine. This system uses model-based estimates of operational parameters to indicate whether the process or machine is operating in a stable state or is in a transition from one state to another.
In some domains, the partial domain knowledge may be available on the relationships between various sensor values. It is important to be able to perform monitoring even in this scenario in a robust fashion detecting recent abnormal behavior in a timely fashion without too many false alarms. Also, training data containing examples of abnormal behavior may not exist. Therefore a need exists for a system to detect recent abnormal behavior using data from multiple channels in a domain with these characteristics.